Sylabus
Course Schedule
Week 1: Course overview. Introduction to the software environment.Week 2: Descriptive vs inferential statistics. Levels of measurement.Week 3: Introduction to probability and probability distributions.Week 4: Sampling variation. Central limit theorem. Confidence intervals (for the mean).Week 5: Statistical hypotheses testing framework. One-sample t-test.Week 6: Independent-samples t-test. Paired-samples t-test.Week 7: Exploring assumptions of parametric tests. Assumption of normality.Week 8: Analysis of variance (within- and between-group variability, F-test, post-hoc tests).Week 9: Correlation analysis (Covariance, Pearson and Spearman correlation coefficients, Scatterplot).Week 10: Linear regression (method of least squares, simple/multiple regression).Week 11: Analysis of categorical data I (confidence interval for a proportion, introduction to crosstabs).Week 12: Analysis of categorical data II (chi-square test of independence, contingency coefficients, residuals).Week 13: Review session.
Anotace
The course will introduce students to the basic data analysis methods used in quantitative social science research. As this is an introductory course, no previous knowledge of statistics is required.
Students will learn and practice basic statistical methods by analyzing sociological survey data in a licenced software called IBM SPSS (each registered student will be provided a licence from the Faculty). After taking this course, students should be able to prepare a data set, perform common data management tasks and analyze quantitative data using basic statistical techniques.
This introductory data analysis course is recommended to students of Erasmus+ and other foreign exchange programs.